Cette thèse s'intéresse en premier lieu à l'analyse des effetsfonctionnels des erreurs dans laconfiguration de FPGAs à base de SRAM. Ces erreurs peuvent provenir deperturbations naturelles(rayonnements, particules) ou d'attaques volontaires, par exemple avecun laser. La famille Virtex IIde Xilinx est utilisée comme premier cas pratique d'expérimentation,puis une comparaison est réaliséeavec la famille AT40K de chez ATMEL. Ceci a permis de mieux comprendrel'impact réel dedifférentes sources de perturbations, et les motifs d'erreur devantréellement être pris en compte pouraméliorer la robustesse d'un circuit implanté sur ce type detechnologie. Cette étude a nécessité ledéveloppement d'outils de conception spécifiques, permettantd'automatiser les analyses. Uneméthodologie innovante est proposée pour l'évaluation de lasensibilité de la mémoire de configurationaux SEUs : une classification des bits de configuration est établie enfonction des effets produits parleur inversion sur le fonctionnement normal de l'application. Cecipermet de déterminer les zones lesplus critiques, autorisant le développement de stratégies deprotection sélectives et à faible coût.

De nos jours, les mémoires sont présentes dans de nombreux circuits intégrés conçus pour des applications électroniques embarquées et occupent une majeure partie de la surface des systèmes sur puce (SoC). Ces mémoires deviennent donc les acteurs principaux du rendement de production. Or, une forte densité d'intégration associée à une complexité élevée des procédés de fabrications rendent ces mémoires toujours plus sensibles aux défauts de fabrications. Afin de mettre en évidence les défaillances survenant dans les mémoires, plusieurs méthodes de test existent. Ces solutions de test couramment utilisées pour les mémoires SRAM sont basées sur la détection de fautes statiques telles que les fautes de collage ou de couplage. Des algorithmes spécifiques, appelés algorithmes March, sont utilisés afin de mettre en évidence ce type de fautes. Cependant, ces solutions de test ne sont pas adaptées à la détection d'un nouveau type de faute apparaissant dans les technologies submicroniques. Ces fautes, appelées fautes dynamiques, sont principalement dues à des défauts de type " ouverts-résistif " et ne se manifestent que dans des configurations très spécifiques. En effet, une séquence d'opérations est nécessaire à la mise en évidence de ces fautes. Le premier objectif de cette thèse a été de proposer des solutions de test permettant la détection de fautes dynamiques dues à des défauts " ouverts-résistifs " dans le driver d'écriture et l'amplificateur de lecture. Une extension sur l'étude des comportements dynamiques face à des variations de procédés de fabrication dans le point mémoire a été proposée. Enfin, la seconde partie de cette thèse fournit de nouvelles solutions de diagnostic, capables de prendre en compte les fautes dynamiques d'une part, et proposant une détection précise des sites fautifs. Ces travaux ont été réalisés en collaboration avec la société Infineon basée à Sophia Antipolis spécialisée dans la conception de mémoires SRAM.

Soft errors are radiation-induced ionization events (induced by energetic particles like alpha particles, cosmic neutron, etc.) that cause transient errors in integrated circuits. The circuit can always recover from such errors as the underlying semiconductor material is not damaged and hence, they are called soft errors. In nanometer technologies, the reduced node capacitance and supply voltage coupled with high packing density and lack of masking mechanisms are primarily responsible for the increased susceptibility of SRAMs towards soft errors. Coupled with these are the process variations (effective length, width, and threshold voltage), which are prominent in scaled-down technologies. Typically, SRAM constitutes up to 90% of the die in microprocessors and SoCs (System-on-Chip). Hence, the soft errors in SRAMs pose a potential threat to the reliable operation of the system. In this work, a soft-error-robust eight-transistor SRAM cell (8T) is proposed to establish a balance between low power consumption and soft error robustness. Using metrics like access time, leakage power, and sensitivity to single event transients (SET), the proposed approach is evaluated. For the purpose of analysis and comparisons the results of 8T cell are compared with a standard 6T SRAM cell and the state-of-the-art soft-error-robust SRAM cells. Based on simulation results in a 65-nm commercial CMOS process, the 8T cell demonstrates higher immunity to SETs along with smaller area and comparable leakage power. A 32-kb array of 8T cells was fabricated in silicon. After functional verification of the test chip, a radiation test was conducted to evaluate the soft error robustness. As SRAM cells are scaled aggressively to increase the overall packing density, the smaller transistors exhibit higher degrees of process variation and mismatch, leading to larger offset voltages. For SRAM sense amplifiers, higher offset voltages lead to an increased likelihood of an incorrect decision. To address this issue, a sense amplifier capable of cancelling the input offset voltage is presented. The simulated and measured results in 180-nm technology show that the sense amplifier is capable of detecting a 4 mV differential input signal under dc and transient conditions. The proposed sense amplifier, when compared with a conventional sense amplifier, has a similar die area and a greatly reduced offset voltage. Additionally, a dual-input sense amplifier architecture is proposed with corroborating silicon results to show that it requires smaller differential input to evaluate correctly.

abstract: Digital systems are increasingly pervading in the everyday lives of humans. The security of these systems is a concern due to the sensitive data stored in them. The physically unclonable function (PUF) implemented on hardware provides a way to protect these systems. Static random-access memories (SRAMs) are designed and used as a strong PUF to generate random numbers unique to the manufactured integrated circuit (IC). Digital systems are important to the technological improvements in space exploration. Space exploration requires radiation hardened microprocessors which minimize the functional disruptions in the presence of radiation. The design highly efficient radiation-hardened microprocessor for enabling spacecraft (HERMES) is a radiation-hardened microprocessor with performance comparable to the commercially available designs. These designs are manufactured using a foundry complementary metal-oxide semiconductor (CMOS) 55-nm triple-well process. This thesis presents the post silicon validation results of the HERMES and the PUF mode of SRAM across process corners. Chapter 1 gives an overview of the blocks implemented on the test chip 25. It also talks about the pre-silicon functional verification methodology used for the test chip. Chapter 2 discusses about the post silicon testing setup of test chip 25 and the validation of the setup. Chapter 3 describes the architecture and the test bench of the HERMES along with its testing results. Chapter 4 discusses the test bench and the perl scripts used to test the SRAM along with its testing results. Chapter 5 gives a summary of the post-silicon validation results of the HERMES and the PUF mode of SRAM. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2017

abstract: In the era of artificial intelligent (AI), deep neural networks (DNN) have achieved accuracy on par with humans on a variety of recognition tasks. However, the high computation and storage requirement of DNN training and inference have posed challenges to deploying or locally training the DNNs on mobile and wearable devices. Energy-efficient hardware innovation from circuit to architecture level is required.In this dissertation, a smart electrocardiogram (ECG) processor is first presented for ECG-based authentication as well as cardiac monitoring. The 65nm testchip consumes 1.06 μW at 0.55 V for real-time ECG authentication achieving equal error rate of 1.7% for authentication on an in-house 645-subject database. Next, a couple of SRAM-based in-memory computing (IMC) accelerators for deep learning algorithms are presented. Two single-array macros titled XNOR-SRAM and C3SRAM are based on resistive and capacitive networks for XNOR-ACcumulation (XAC) operations, respectively. XNOR-SRAM and C3SRAM macros in 65nm CMOS achieve energy efficiency of 403 TOPS/W and 672 TOPS/W, respectively. Built on top of these two single-array macro designs, two multi-array architectures are presented. The XNOR-SRAM based architecture titled “Vesti” is designed to support configurable multibit activations and large-scale DNNs seamlessly. Vesti employs double-buffering with two groups of in-memory computing SRAMs, effectively hiding the write latency of IMC SRAMs. The Vesti accelerator in 65nm CMOS achieves energy consumption of <20 nJ for MNIST classification and <40μJ for CIFAR-10 classification at 1.0 V supply. More recently, a programmable IMC accelerator (PIMCA) integrating 108 C3SRAM macros of a total size of 3.4 Mb is proposed. The28nm prototype chip achieves system-level energy efficiency of 437/62 TOPS/W at 40 MHz, 1 V supply for DNNs with 1b/2b precision. In addition to the IMC works, this dissertation also presents a convolutional neural network (CNN) learning processor, which accelerates the stochastic gradient descent (SGD) with momentum based training algorithm in 16-bit fixed-point precision. The65nm CNN learning processor achieves peak energy efficiency of 2.6 TOPS/W for16-bit fixed-point operations, consuming 10.45 mW at 0.55 V. In summary, in this dissertation, several hardware innovations from circuit to architecture level are presented, exploiting the reduced algorithm complexity with pruning and low-precision quantization techniques. In particular, macro-level and system-level SRAM based IMC works presented in this dissertation show that SRAM based IMC is one of the promising solutions for energy-efficient intelligent systems. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

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Indiana University-Purdue University Indianapolis (IUPUI) / In this dissertation, a beyond-CMOS approach to Static Random Access Memory (SRAM) design is investigated using exploratory transistors including Tunnel Field Effect Transistor (TFET), Carbon Nanotube Field Effect Transistor (CNFET) and Graphene NanoRibbon Field Effect Transistor (GNRFET). A Figure-of-Merit (FOM) based comparison of 6-transistor (6T) and a modified 8-transistor (8T) single-port SRAMs designed using exploratory devices, and contemporary devices such as a FinFET and a CMOS process, highlighted the performance benefits of GNRFETs and power benefits of TFETs. The results obtained from the this work show that GNRFET-based SRAM have very high performance with a worst-case memory access time of 27.7 ps for a 16x4-bit 4-word array of 256-bitcells. CNFET-based SRAM bitcell consume the lowest average power during read/write simulations at 3.84 uW, while TFET-based SRAM bitcell show the best overall average and static power consumption at 4.79 uW and 57.8 pW respectively. A comparison of these exploratory devices with FinFET and planar CMOS showed that FinFET-based SRAM bitcell consumed the lowest static power at 39.8 pW and CMOS-based SRAM had the best read, write and hold static noise margins at 201 mV, 438 mV and 413 mV respectively. Further, the modification of 8T-SRAMs via dual wordlines for individually controlling read and write operations for uni-directional transistors TFET and CNFET show improvement in read static noise margin (RSNM). In dual wordline CNFET 8T-SRAM, an RSNM improvement of approximately 23.6x from 6 mV to 142 mV was observed by suppressing the read wordline (RWL) from a nominal supply of 0.71 V down to 0.61 V. In dual wordline TFET 8T-SRAM, an RSNM improvement of approximately 16.2x from 5 mV to 81 mV was observed by suppressing the RWL from a nominal supply of 0.6 V down to 0.3 V. Next, the dissertation explores whether the robustness of SRAM arrays can be improved. Specifically, the robustness related to noise margin during the write operation was investigated by implementing a negative bitline (NBL) voltage scheme. NBL improves the write static noise margin (WSNM) of the SRAM bitcells in the row of the array to which the data is written during a write operation. However, this may cause degraded hold static noise margin (HSNM) of un-accessed cells in the array. Applying a negative wordline voltage (NWL) on un-accessed cells during NBL shows that the NWL can counter the degraded HSNM of un-accessed cells due to NBL. The scheme, titled as NBLWL, also allows the supply of a lower NBL, resulting in higher WSNM and write-ability benefits of accessed row. By applying a complementary negative wordline voltage to counter the half-select condition in columns, the WSNM of cells in accessed rows was boosted by 10.9% when compared to a work where no negative bitline was applied. In addition, the HSNM of un-accessed cells remain the same as in the case where no negative bitline was implemented. Essentially, a 10.9% boost in WSNM without any degradation of HSNM in un-accessed cells is observed. The dissertation also focuses on the impact of process-related variations in SRAM arrays to correlate and characterize silicon data to simulation data. This can help designers remove pessimistic margins that are placed on critical signals to account for expected process variation. Removing these pessimistic margins on critical data paths that dictate the memory access time results in performance benefits for the SRAM array. This is achieved via an in-situ silicon monitor titled SRAM process and ageing sensor (SPAS), which can be used for silicon and ageing characterization, and silicon debug. The SPAS scheme is based on a process variation tolerant technique called RAZOR that compares the data arriving on the output of the sense amplifiers during the read operation. This scheme can estimate the impact of process variation and ageing induced slow-down on critical path during read operation of an array with high accuracy. The estimation accuracy in a commercially available 65nm CMOS technology for a 16x16 array at TT, and global SS and FF corners at nominal supply and testing temperature were found to be 99.2%, 94.9% and 96.5% respectively. Finally, redundant columns, an architectural-level scheme for tolerating failing SRAM bitcells in arrays without compromising performance and yield, is studied. Redundant columns are extra columns that are programmed when bitcells in the regular columns of an array are slower or have higher leakage than expected post-silicon. The regular columns are often permanently disabled and remain unused for the chip lifetime once redundant columns are enabled. In the SRRC scheme proposed in this thesis, the regular columns are only temporarily disabled, and re-used at a later time in chip life cycle once the previously awakened redundant columns become slower than the disabled regular columns. Essentially, the scheme can identify and temporarily disable the slowest column in an array until other mitigating factors slow down active columns. This allows the array to operate at a memory access time closer to the target access time regardless of other mitigating factors slowing down bitcells in arrays during chip life cycle. An approximate 76.4% reduction in memory access time was observed from a 16x16 array from simulations in a commercially available 65nm CMOS technology with respect to a work where no redundancy was employed.

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